{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import settings\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    # resp = requests.get(settings.LOTTO_DOWNLOAD_URL)\n",
    "    resp = requests.get(\"http://106.13.233.100:3002/getLimitData/120\")\n",
    "    if resp.status_code == 200:\n",
    "        respJson = resp.json()\n",
    "        l_len = len(respJson)\n",
    "        # print(l_len)\n",
    "        respJson.reverse()\n",
    "        data = []\n",
    "        for i in range(l_len):\n",
    "          d = respJson[i]\n",
    "          data.append([d[\"issueNo\"],d[\"one\"],d[\"two\"],d[\"three\"],d[\"four\"],d[\"five\"],0,0])\n",
    "        print(data)\n",
    "    else:\n",
    "        raise Exception('获取数据失败！')\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 5)\n",
    "ypoints = np.random.randint(100, size=(5)) * 0.001\n",
    "\n",
    "plt.plot(x, ypoints, label=\"test1\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.165 0.123 0.174 0.183 0.273]\n"
     ]
    }
   ],
   "source": [
    "x=np.random.randint(100, size=(5))\n",
    "x = x * 0.003\n",
    "print(x)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "239cd0f6924d34dc9a29cd51ff042db2679e9319f05891fd61214d5969f7bbf8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
